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Unit of study_

HTIN4006: Foundations of Healthcare Data Science

The transformation of medicine and health by big data and artificial intelligence is already underway, with ever more routine data collection and its linkage through electronic means. Herein lies the potential to supply real-time personalised healthcare, deep clinical phenotyping and diagnostic capabilities, and prognostic predictions of disease and intervention outcomes. Data science techniques underpin these approaches. This unit will provide a deep dive into understanding the entire end-to-end data cycle / pipeline of healthcare data: from its acquisition (e.g., health records, imaging, sensors etc), to its processing (e.g., cleaning, feature extraction, data linkage etc), to analysing the data (e.g., decision support / computer aided diagnosis) and finally to use the data for prediction (e.g., prognosis and modelling). We will also study the importance of using the data to its stakeholders (patients, clinicians, society etc.) by taking into account of the ethics, privacy, security and measurable benefits from the use of the data. On completion of this unit, students will have a solid understanding of how the healthcare data is now being exploited, through data science principles and tools, to provide improved healthcare delivery. Students will also learn practical skills in healthcare data analysis using Python programming language.

Code HTIN4006
Academic unit Computer Science
Credit points 6
Prerequisites:
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None
Corequisites:
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Enrolment in a thesis unit. INFO4001 or INFO4911 or INFO4991 or INFO4992 or AMME4111 or BMET4111 or CHNG4811 or CIVL4022 or ELEC4712 or COMP4103 or SOFT4103 or DATA4103 or ISYS4103
Prohibitions:
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HTIN5006

The learning outcomes for this unit will be available two weeks before the first day of teaching.

Unit outlines

Unit outlines will be available 1 week before the first day of teaching for the relevant session.

There are no unit outlines available online for previous years.